The novel calcium-silicate-hydrate(C-S-H)/paraffin composite phase change materials were synthesized using a discontinuous two-step nucleation method. Initially, the C-S-H precursor is separated and dried, followed by immersion in an aqueous environment to transform it into C-S-H. This two-step nucleation approach results in C-S-H with a specific surface area of 497.2 m²/g, achieved by preventing C-S-H foil overlapping and refining its pore structure. When impregnated with paraffin, the novel C-S-H/paraffin composite exhibits superior thermal properties, such as a higher potential heat value of 148.3 J/g and an encapsulation efficiency of 81.6%, outperforming conventional C-S-H. Moreover, the composite material demonstrates excellent cyclic performance, indicating its potential for building thermal storage compared to other paraffin-based composites. Compared with the conventional method, this simple technology, which only adds conversion and centrifugation steps, does not negatively impact preparation costs, the environment, and resource consumption. This study provides valuable theoretical insights for designing thermal storage concrete materials and advancing building heat management.

An advanced discrete element servomechanism that can simultaneously and independently control the evolution equations of six stress and strain components without introducing severe stress concentration is implemented. Such a comprehensive series of discrete element method simulations of both drained and undrained behavior of transversely isotropic sandy soils are successfully conducted in the true triaxial setting. During the simulation process, the evolution patterns of the load-bearing structure of the granular specimen are tracked using a contact-normal-based fabric tensor. The simulation results show that sandy soils exhibit more significant non-coaxiality between the loading direction and the major principal direction of the fabric tensor under extension than under compression. Therefore, the fabric of the sandy soils under extension has a stronger tendency to evolve toward the loading direction than that under compression, causing a more significant disturbance to the load-bearing structure. Consequently, compared with the extension loading condition, the transversely isotropic specimen under compression exhibits a higher shear strength and stronger dilatancy under drained conditions and a stronger liquefaction resistance under undrained conditions.

To study the ground motion intensity measures(IMs)suitable for the design of seismic performance with a focus on longitudinal resistance in tunnel structures, 21 different seismic intensity parameters are selected for nonlinear calculation and analysis of tunnel structures, in order to determine the optimal IM for the longitudinal seismic performance of tunnel structures under different site conditions. An improved nonlinear beam-spring model is developed to calculate the longitudinal seismic response of tunnels. The PQ-Fiber model is used to simulate the longitudinal nonlinear behavior of tunnel structures and the tangential interactions between the tunnel and the soil is realized by load in the form of moment. Five different site types are considered and 21 IMs is evaluated against four criteria: effectiveness, practicality, usefulness, and sufficiency. The results indicate that the optimal IMs are significantly influenced by the site conditions. Specifically, sustained maximum velocity(V_SM)emerges as the optimal IM for circular tunnels in soft soil conditions(Case Ⅰ sites), peak ground velocity(V_PG)is best suited for Case Ⅱ sites, sustained maximum acceleration(A_SM)is ideal for both Case Ⅲ and Case Ⅴ sites, and peak ground acceleration(A_PG)for Case Ⅳ sites. As site conditions transition from Case Ⅰ to Case Ⅴ, from soft to hard, the applicability of acceleration-type intensity parameters gradually decreases, while the applicability of velocity-type intensity parameters gradually increases.

To investigate the effects of plateau environments on driving fatigue, heart rate and electroencephalogram(EEG)signals were chosen as indicators to characterize driving fatigue. The study analyzed the variation in these indicators as drivers transitioned into fatigued stages. By examining the sample entropy of EEG signals and the heart rate variation coefficient, a complex indicator of driving fatigue(CIDF)was established using principal component analysis to overcome the limitations of single-indicator methods. According to the CIDF values, the driving fatigue states in plateau areas were subdivided into three categories, including alertness, mild fatigue, and severe fatigue, by cluster analysis. Optimal binning determined thresholds for different driving fatigue states, which were validated through variance analysis. The results indicate that the CIDF values effectively distinguish the driving fatigue states of drivers in plateau areas. The CIDF thresholds for the alertness and the mild fatigue states are 0.34 and 0.50, respectively. A CIDF value greater than 0.50 indicates that the driver is in a severe fatigue state.

The floating photovoltaic membrane prototype developed by Ocean Sun was selected as a reference object, and a 1∶40 scale laboratory model was designed and produced to further explore the impact of inflow conditions on the hydrodynamic properties of the membrane structure. By conducting free attenuation tests, results showed that the inflow has only a slight effect on the natural frequencies of the heave, pitch, and surge of the membrane structure. This finding shows that the dynamic properties of the membrane structure remain essentially stable under different inflow conditions. The results of further regular and irregular wave hydrodynamic experiments show that, compared with the control group, the response of the membrane structure under inflow conditions in terms of heave, pitch, surge, and heave acceleration motions is relatively gentle, whereas the response of the membrane structure to the mooring force is strong. Especially when the waves are irregular, the inflow conditions have a more significant impact on the membrane structure, which may lead to more complex response changes in the structure. Therefore, in the actual engineering design process, the impact of inflow conditions on the behavior of the membrane structure must be fully considered, and appropriate engineering measures must be taken to ensure the safety and stability of the structure.

To reduce the shielding effect of hardened layers on electrical resistivity tomography, a ratio method based on the distortion correction principle and the isolation coefficient is proposed. The effects of the resistivity and thickness of hardened concrete layers on the detection of target objects are explored. Both numerical simulations and indoor tank tests indicate that when the ratio method is employed to correct the original collected data, the maximum allowable error for the isolation coefficient should not exceed 1%. Notably, when the ratio of hardened layer thickness to electrode spacing does not exceed 1, correction through this method significantly enhances the recognition capability of target objects. However, when the hardened layer thickness is greater than the electrode spacing by a factor of 2 or more, the ratio method cannot achieve satisfactory results. The case study of flood control engineering detection in the Zhangxi section of the Huangpen River in Dongzhi County demonstrates that the detection effect after correction by the ratio method is comparable to that for the adjacent unhardened pavement, and the influence of the hardened layer is obviously weakened, resulting in more reliable results.

The road traffic network contains a large number of bridges, and calculating bridge damage using refined models demands significant time and resources. Therefore, developing a rapid evaluation method for the seismic capacity of regular bridges has become a crucial scientific challenge. This study presents an approach in which the ductile column is represented by a single degree-of-freedom model with elastic-plastic constitutive characteristics. Utilizing an uncoupled multivariate power function model and a plastic hinge model, a multidimensional power function model for section hierarchical curvature is constructed. Subsequently, the seismic multistage damage constitutive model(SMSD-CM)of member hierarchy is deduced and calibrated through theoretical methods. This model efficiently derives the trilinear constitutive model of components by inputting several crucial parameters. The SMSD-CM accurately simulates the hysteretic curve and displacement time-history under actual seismic conditions and aligns well with pushover analysis results from tests. The efficiency, ease of operation, and accuracy make the model suitable for rapid evaluation of the seismic capacity of regular bridges within the road traffic network.

To address the limitation of single acceleration sensor signals in effectively reflecting the health status of rolling bearings, a rolling bearing fault diagnosis method based on the fusion of data-level and feature-level information was proposed. First, according to the impact characteristics of rolling bearing faults, correlation kurtosis rules were designed to guide the weight distribution of multi-sensor signals. These rules were then combined with a weighted fusion method to obtain high-quality data-level fusion signals. Subsequently, a feature-fusion convolutional neural network(FFCNN)that merges the one-dimensional(1D)features extracted from the fused signal with the two-dimensional(2D)features extracted from the wavelet time-frequency spectrum was designed to obtain a comprehensive representation of the health status of rolling bearings. Finally, the fused features were fed into a Softmax classifier to complete the fault diagnosis. The results show that the proposed method exhibits an average test accuracy of over 99.00% on the two rolling bearing fault datasets, outperforming other comparison methods. Thus, the method can be effectively utilized for diagnosing rolling bearing faults.

To enhance the piezoelectric performance of piezoelectric polymer thin films in general, hybrid polyvinylidene difluoride(PVDF)and nanosized barium titanate(BaTiO₃)piezoelectric films were prepared and their piezoelectric performance examined. The hybrid nanofibers were fabricated via electrospinning at an external voltage of 15 kV. The nonwoven fabrics were collected using a roller collection device, and their morphological structures were analyzed via scanning electron microscopy. The crystal structures of these piezoelectric films were characterized via micro-Raman spectroscopy. β-phase of the composite nanofiber membrane almost increased to twice owing to the addition of BaTiO₃ nanoparticles. Compared with pure, electrospun PVDF piezoelectric film, the piezoelectric characteristics of the hybrid piezoelectric films were considerably enhanced because of the additional BaTiO₃ nanoparticles. The maximum instantaneous open-circuit voltage of the hybrid PVDF-BaTiO₃ nanofibers film can be high up to 80 V. The high-performance hybrid piezoelectric films exhibited notable prospects for applications in wearable electronic textiles.

Considering the special walking behavior of astronauts on the lunar surface, to reduce the impact on their bones and improve safety during extravehicular operations and walking, a magnetorheological(MR)damping mechanism of power assisted transmission joint used in a new type spacesuit is proposed. In order to improve the damping performance of the MR damper, the influence of the damper’s structural parameters on both the output and dynamic adjustable range of the damping torque is examined. According to the theoretical mechanical model, the output damping torque is calculated, the finite element method is used to conduct numerical tests. At the same time, the structural parameters of the damper are optimized by the response surface methods. The results indicate that the simulated torque aligns with the theoretically designed torque, and the damping characteristics of the optimized structure are effectively improved by the response surface method. Compared with the initial structure, the damping torque is increased by 10.8%, and the dynamic adjustable range is expanded by 52.9%.

To solve the low efficiency of approximate queries caused by the large sizes of the knowledge graphs in the real world, an embedding-based approximate query method is proposed. First, the nodes in the query graph are classified according to the degrees of approximation required for different types of nodes. This classification transforms the query problem into three constraints, from which approximate information is extracted. Second, candidates are generated by calculating the similarity between embeddings. Finally, a deep neural network model is designed, incorporating a loss function based on the high-dimensional ellipsoidal diffusion distance. This model identifies the distance between nodes using their embeddings and constructs a score function. k nodes are returned as the query results. The results show that the proposed method can return both exact results and approximate matching results. On datasets DBLP(DataBase systems and Logic Programming)and FUA-S(Flight USA Airports-Sparse), this method exhibits superior performance in terms of precision and recall, returning results in 0.10 and 0.03 s, respectively. This indicates greater efficiency compared to PathSim and other comparative methods.

To systematically incorporate multiple influencing factors, the coupled-state frequency memory(Co-SFM)network is proposed. This model integrates Copula estimation with neural networks, fusing multilevel data information, which is then fed into downstream learning modules. Co-SFM employs an upstream fusion module to incorporate multilevel data, thereby constructing a macro-plate-micro data structure. This configuration helps identify and integrate characteristics from different data levels, facilitating a deeper understanding of the internal links within the financial system. In the downstream model, Co-SFM uses a state-frequency memory network to mine hidden frequency information within stock prices, and the multifrequency patterns of sequential data are modeled. Empirical results show that Co-SFM’s prediction accuracy for stock price trends is significantly better than that of other models. This is especially evident in multistep medium and long-term trend predictions, where integrating multilevel data results in notably improved accuracy.